Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX
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Cross Attention in Transformers | 100 Days Of Deep Learning | CampusX
Cross Attention is a mechanism in transformer models where the attention is applied between different sequences, typically between the output of one layer and the input of another. It allows the model to focus on relevant parts of the input sequence while generating output, making it crucial in tasks like translation, where the decoder attends ...
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